We propose a method for using the scoring values of passages to effectively retrieve documents in a Question Answering system. For this, we suggest evaluation function that considers proximity between each question terms in passage. And using this evaluation function , we extract a documents which involves scoring values in the highest collection, as a suitable document for question. The propos...

We characterize which scoring rules are Maskin-monotonic for each social choice problem as a function of the number of agents and the number of alternatives. We show that a scoring rule is Maskin-monotonic if and only if it satisfies a certain unanimity condition. Since scoring rules are neutral, Maskin-monotonicity turns out to be equivalent to Nash-implementability within the class of scoring...

MOTIVATION Accurately predicting the binding affinities of large sets of diverse protein-ligand complexes is an extremely challenging task. The scoring functions that attempt such computational prediction are essential for analysing the outputs of molecular docking, which in turn is an important technique for drug discovery, chemical biology and structural biology. Each scoring function assumes...

Training machine learning algorithms with protein-ligand descriptors has recently gained considerable attention to predict binding constants from atomic coordinates. Starting from a series of recent reports stating the advantages of this approach over empirical scoring functions, we could indeed reproduce the claimed superiority of Random Forest and Support Vector Machine-based scoring function...

We developed Græmlin 2.0, a new multiple network aligner with (1) a novel scoring function that can use arbitrary features of a multiple network alignment, such as protein deletions, protein duplications, protein mutations, and interaction losses; (2) a parameter learning algorithm that uses a training set of known network alignments to learn parameters for our scoring function and thereby adap...

We developed Graemlin 2.0, a new multiple network aligner with (1) a new multi-stage approach to local network alignment; (2) a novel scoring function that can use arbitrary features of a multiple network alignment, such as protein deletions, protein duplications, protein mutations, and interaction losses; (3) a parameter learning algorithm that uses a training set of known network alignments t...

We present here an algorithm for domain specific all-words WSD. The scoring function to rank the senses is inspired by the quadratic energy expression of Hopfield network, a well studied expression in neural networks. The scoring function is employed by a greedy iterative disambiguation algorithm that uses only the wordsdisambiguated-so-far to disambiguate the current word in focus. The combina...

A protein-protein docking procedure traditionally consists in two successive tasks: a search algorithm generates a large number of candidate conformations mimicking the complex existing in vivo between two proteins, and a scoring function is used to rank them in order to extract a native-like one. We have already shown that using Voronoi constructions and a well chosen set of parameters, an acc...

We report a C-atom-based scoring function, named OPUS-CSF, for ranking protein structural models. Rather than using traditional Boltzmann formula, we built a scoring function (CSF score) based on the native distributions (derived from the entire PDB) of coordinate components of mainchain C (carbonyl) atoms on selected residues of peptide segments of 5, 7, 9, and 11 residues in length. In testin...

The development of an energy or scoring function for protein structure prediction is greatly enhanced by testing the function on a set of computer-generated conformations (decoys) to determine whether it can readily distinguish native-like conformations from nonnative ones. We have created "Decoys 'R' Us," a database containing many such sets of conformations, to provide a resource that allows ...